AI Automation Engineer

Requirements

• Required:
• 3-5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation
• Hands-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP)
• Demonstrated experience with the full AI/ML model lifecycle: data preparation, training, validation, deployment, and monitoring
• Experience in a regulated industry (healthcare, insurance, or financial services) with workin

Benefits

Additional Information

Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together. The Artificial Intelligence & Automation Engineer is a hands-on technical contributor on the Data & Technology Solutions team, responsible for designing, building, and deploying intelligent AI systems and automated workflows that drive operational efficiency and elevate the quality of care for our Medicare Advantage members. You will partner closely with AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams to translate complex business problems - from claims processing and prior authorization to member communication and revenue integrity - into scalable, production-grade solutions. This role directly influences our ability to reduce administrative burden, accelerate payment accuracy, and create smarter, faster member experiences. Job Duties / Responsibilities Design and deploy production-grade AI and machine learning models. Build, test, and deploy predictive and generative AI models - including NLP, classification, regression, and computer vision - into cloud-based environments optimized for Medicare Advantage workloads such as claims processing, risk adjustment, and member communication. Maintain rigorous monitoring and drift detection to preserve model integrity over time. Build and operate intelligent process automation solutions. Identify and automate high-volume, repetitive business processes using RPA platforms (e.g., UiPath, Power Automate) and orchestration tools (e.g., Airflow, Prefect). Evaluate automation ROI, build fault-tolerant workflows with proper error handling and alerting, and maintain automation bots and pipelines as business needs evolve . Develop and maintain AI/ML data infrastructure and pipelines. Build robust ETL and feature engineering pipelines that support model training, validation, and real-time inference. Manage AI/ML infrastructure to enable efficient experimentation, retraining, and monitoring so that models perform accurately and equitably in our regulated production environment. Integrate AI models and automation services into enterprise products. Connect deployed models and automation services into existing systems via REST APIs and microservices, ensuring full adherence to HIPAA security and privacy standards. Containerize and deploy using Docker and Kubernetes with CI/CD pipelines that ensure reproducible, reliable releases. Implement LLM-powered applications and generative AI capabilities. Integrate large language models into clinical and operational workflows using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). Leverage agentic AI frameworks to build multi-step automation solutions that deliver end-to-end workflow transformation for high-impact processes such as clinical document intelligence and member engagement. Monitor, optimize , and ensure system reliability. Continuously test and tune models, pipelines, and automation bots to improve accuracy, latency, and scalability. Implement monitoring and alerting frameworks to proactively identify degradation or data quality issues before they affect member outcomes or operational throughput. Collaborate cross-functionally and advance a culture of AI innovation. Partner with AI Scientists, Product Managers, Clinical stakeholders, and business analysts to translate requirements into technical specifications. Contribute to knowledge sharing through documentation and code reviews, and evaluate emerging AI technologies - including responsible AI practices such as bias detection and model explainability. Supervisory Responsibilities This is an individual contributor role with no direct supervisory responsibility. The engineer is expected to provide informal technical mentorship to peers and junior team members through code reviews, documentation, and collaborative problem-solving. Job Requirements

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